Table REF  shows how SPIRE changes the training distributions for \(p = 0.9\)  and \(p = 0.1\)  (see Appendix REF ).
For \(p = 0.9\) , it succeeds at both of its goals.
For \(p = 0.1\) , it produces the balanced distribution, but it does add the potential for new SPs because P(Main | Removed an object) = 0 and P(Main | Added an object) = 1.
We contrast SPIRE to a closely related augmentation-based method called QCEC [1]}, which removes either Main or Spurious uniformly at random from each image.
For both values of \(p\) , QCEC does not make Main and Spurious independent and adds the potential for new SPs.
This example highlights the fact that, while prior work has used similar counterfactuals for data augmentation, SPIRE uses them in a fundamentally different way.
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